Overview

Dataset statistics

Number of variables32
Number of observations1102
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory158.0 KiB
Average record size in memory146.8 B

Variable types

Numeric15
Categorical16
Boolean1

Alerts

Age is highly correlated with JobLevel and 3 other fieldsHigh correlation
MonthlyIncome is highly correlated with Age and 4 other fieldsHigh correlation
TotalWorkingYears is highly correlated with Age and 7 other fieldsHigh correlation
YearsAtCompany is highly correlated with Age and 6 other fieldsHigh correlation
YearsInCurrentRole is highly correlated with JobLevel and 4 other fieldsHigh correlation
YearsSinceLastPromotion is highly correlated with TotalWorkingYears and 3 other fieldsHigh correlation
YearsWithCurrManager is highly correlated with JobLevel and 4 other fieldsHigh correlation
JobLevel is highly correlated with Age and 6 other fieldsHigh correlation
Department is highly correlated with EducationField and 1 other fieldsHigh correlation
StockOptionLevel is highly correlated with MaritalStatusHigh correlation
JobRole is highly correlated with JobLevel and 4 other fieldsHigh correlation
EducationField is highly correlated with Department and 1 other fieldsHigh correlation
MaritalStatus is highly correlated with StockOptionLevelHigh correlation
PercentSalaryHike is highly correlated with PerformanceRatingHigh correlation
PerformanceRating is highly correlated with PercentSalaryHikeHigh correlation
df_index is uniformly distributed Uniform
df_index has unique values Unique
NumCompaniesWorked has 144 (13.1%) zeros Zeros
TrainingTimesLastYear has 35 (3.2%) zeros Zeros
YearsAtCompany has 28 (2.5%) zeros Zeros
YearsInCurrentRole has 179 (16.2%) zeros Zeros
YearsSinceLastPromotion has 444 (40.3%) zeros Zeros
YearsWithCurrManager has 192 (17.4%) zeros Zeros

Reproduction

Analysis started2022-10-02 07:48:09.547244
Analysis finished2022-10-02 07:48:35.325780
Duration25.78 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct1102
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean734.8094374
Minimum0
Maximum1469
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2022-10-02T13:18:35.398617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile70.15
Q1356.25
median738.5
Q31113.75
95-th percentile1397.95
Maximum1469
Range1469
Interquartile range (IQR)757.5

Descriptive statistics

Standard deviation428.7195349
Coefficient of variation (CV)0.5834431528
Kurtosis-1.225935839
Mean734.8094374
Median Absolute Deviation (MAD)379.5
Skewness-0.005081257521
Sum809760
Variance183800.4396
MonotonicityNot monotonic
2022-10-02T13:18:35.494331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71
 
0.1%
1751
 
0.1%
3371
 
0.1%
3581
 
0.1%
4151
 
0.1%
10961
 
0.1%
10551
 
0.1%
8751
 
0.1%
2921
 
0.1%
11621
 
0.1%
Other values (1092)1092
99.1%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
91
0.1%
111
0.1%
ValueCountFrequency (%)
14691
0.1%
14681
0.1%
14661
0.1%
14651
0.1%
14631
0.1%
14621
0.1%
14611
0.1%
14591
0.1%
14571
0.1%
14511
0.1%

Age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct43
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.90018149
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2022-10-02T13:18:35.593067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile23.05
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.079576545
Coefficient of variation (CV)0.2460577747
Kurtosis-0.3567285768
Mean36.90018149
Median Absolute Deviation (MAD)6
Skewness0.4087310495
Sum40664
Variance82.43871023
MonotonicityNot monotonic
2022-10-02T13:18:35.679834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3559
 
5.4%
3458
 
5.3%
3157
 
5.2%
2952
 
4.7%
3651
 
4.6%
3348
 
4.4%
3846
 
4.2%
4044
 
4.0%
3043
 
3.9%
3740
 
3.6%
Other values (33)604
54.8%
ValueCountFrequency (%)
186
 
0.5%
198
 
0.7%
208
 
0.7%
2111
 
1.0%
2211
 
1.0%
2312
 
1.1%
2418
1.6%
2517
1.5%
2627
2.5%
2737
3.4%
ValueCountFrequency (%)
602
 
0.2%
599
0.8%
5812
1.1%
573
 
0.3%
568
0.7%
5518
1.6%
5414
1.3%
5312
1.1%
5212
1.1%
5113
1.2%

DailyRate
Real number (ℝ≥0)

Distinct755
Distinct (%)68.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean796.7259528
Minimum102
Maximum1496
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2022-10-02T13:18:35.769594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile167
Q1464.25
median783
Q31152.5
95-th percentile1424.9
Maximum1496
Range1394
Interquartile range (IQR)688.25

Descriptive statistics

Standard deviation404.1147143
Coefficient of variation (CV)0.5072192174
Kurtosis-1.205146758
Mean796.7259528
Median Absolute Deviation (MAD)348.5
Skewness0.01381610407
Sum877992
Variance163308.7023
MonotonicityNot monotonic
2022-10-02T13:18:35.859354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5305
 
0.5%
6915
 
0.5%
6884
 
0.4%
14904
 
0.4%
5894
 
0.4%
4654
 
0.4%
3344
 
0.4%
4084
 
0.4%
9214
 
0.4%
12254
 
0.4%
Other values (745)1060
96.2%
ValueCountFrequency (%)
1021
0.1%
1031
0.1%
1041
0.1%
1051
0.1%
1061
0.1%
1071
0.1%
1091
0.1%
1112
0.2%
1151
0.1%
1162
0.2%
ValueCountFrequency (%)
14961
 
0.1%
14951
 
0.1%
14921
 
0.1%
14904
0.4%
14881
 
0.1%
14853
0.3%
14821
 
0.1%
14802
0.2%
14792
0.2%
14761
 
0.1%

DistanceFromHome
Real number (ℝ≥0)

Distinct29
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.123411978
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2022-10-02T13:18:35.948117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.023875361
Coefficient of variation (CV)0.8794818628
Kurtosis-0.1261967128
Mean9.123411978
Median Absolute Deviation (MAD)5
Skewness0.9791028178
Sum10054
Variance64.38257581
MonotonicityNot monotonic
2022-10-02T13:18:36.024911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2159
14.4%
1158
14.3%
1071
 
6.4%
966
 
6.0%
759
 
5.4%
359
 
5.4%
857
 
5.2%
651
 
4.6%
447
 
4.3%
545
 
4.1%
Other values (19)330
29.9%
ValueCountFrequency (%)
1158
14.3%
2159
14.4%
359
 
5.4%
447
 
4.3%
545
 
4.1%
651
 
4.6%
759
 
5.4%
857
 
5.2%
966
6.0%
1071
6.4%
ValueCountFrequency (%)
2923
2.1%
2814
1.3%
2710
0.9%
2618
1.6%
2518
1.6%
2418
1.6%
2319
1.7%
2212
1.1%
2114
1.3%
2018
1.6%

Education
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
3
430 
4
296 
2
208 
1
129 
5
 
39

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1102
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row2
5th row4

Common Values

ValueCountFrequency (%)
3430
39.0%
4296
26.9%
2208
18.9%
1129
 
11.7%
539
 
3.5%

Length

2022-10-02T13:18:36.102703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T13:18:36.194397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3430
39.0%
4296
26.9%
2208
18.9%
1129
 
11.7%
539
 
3.5%

Most occurring characters

ValueCountFrequency (%)
3430
39.0%
4296
26.9%
2208
18.9%
1129
 
11.7%
539
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1102
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3430
39.0%
4296
26.9%
2208
18.9%
1129
 
11.7%
539
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common1102
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3430
39.0%
4296
26.9%
2208
18.9%
1129
 
11.7%
539
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3430
39.0%
4296
26.9%
2208
18.9%
1129
 
11.7%
539
 
3.5%
Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
4
340 
3
337 
2
219 
1
206 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1102
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row2
4th row3
5th row1

Common Values

ValueCountFrequency (%)
4340
30.9%
3337
30.6%
2219
19.9%
1206
18.7%

Length

2022-10-02T13:18:36.265323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T13:18:36.338128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4340
30.9%
3337
30.6%
2219
19.9%
1206
18.7%

Most occurring characters

ValueCountFrequency (%)
4340
30.9%
3337
30.6%
2219
19.9%
1206
18.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1102
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4340
30.9%
3337
30.6%
2219
19.9%
1206
18.7%

Most occurring scripts

ValueCountFrequency (%)
Common1102
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4340
30.9%
3337
30.6%
2219
19.9%
1206
18.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4340
30.9%
3337
30.6%
2219
19.9%
1206
18.7%

HourlyRate
Real number (ℝ≥0)

Distinct71
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.92558984
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2022-10-02T13:18:36.419910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33.05
Q148
median66
Q384
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)36

Descriptive statistics

Standard deviation20.32899028
Coefficient of variation (CV)0.3083626606
Kurtosis-1.200926404
Mean65.92558984
Median Absolute Deviation (MAD)18
Skewness-0.02659332984
Sum72650
Variance413.2678459
MonotonicityNot monotonic
2022-10-02T13:18:36.515654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4226
 
2.4%
6624
 
2.2%
5424
 
2.2%
5622
 
2.0%
9822
 
2.0%
8420
 
1.8%
4319
 
1.7%
5219
 
1.7%
4819
 
1.7%
9219
 
1.7%
Other values (61)888
80.6%
ValueCountFrequency (%)
3014
1.3%
3111
1.0%
3216
1.5%
3315
1.4%
347
0.6%
3513
1.2%
3616
1.5%
3716
1.5%
387
0.6%
399
0.8%
ValueCountFrequency (%)
10014
1.3%
9915
1.4%
9822
2.0%
9716
1.5%
9618
1.6%
9517
1.5%
9419
1.7%
9312
1.1%
9219
1.7%
9114
1.3%

JobInvolvement
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
3
658 
2
285 
4
104 
1
 
55

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1102
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3658
59.7%
2285
25.9%
4104
 
9.4%
155
 
5.0%

Length

2022-10-02T13:18:36.602422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T13:18:36.674230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3658
59.7%
2285
25.9%
4104
 
9.4%
155
 
5.0%

Most occurring characters

ValueCountFrequency (%)
3658
59.7%
2285
25.9%
4104
 
9.4%
155
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1102
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3658
59.7%
2285
25.9%
4104
 
9.4%
155
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common1102
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3658
59.7%
2285
25.9%
4104
 
9.4%
155
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3658
59.7%
2285
25.9%
4104
 
9.4%
155
 
5.0%

JobLevel
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
1
412 
2
405 
3
157 
4
82 
5
46 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1102
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row5
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1412
37.4%
2405
36.8%
3157
 
14.2%
482
 
7.4%
546
 
4.2%

Length

2022-10-02T13:18:36.739056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T13:18:36.812859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1412
37.4%
2405
36.8%
3157
 
14.2%
482
 
7.4%
546
 
4.2%

Most occurring characters

ValueCountFrequency (%)
1412
37.4%
2405
36.8%
3157
 
14.2%
482
 
7.4%
546
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1102
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1412
37.4%
2405
36.8%
3157
 
14.2%
482
 
7.4%
546
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Common1102
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1412
37.4%
2405
36.8%
3157
 
14.2%
482
 
7.4%
546
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1412
37.4%
2405
36.8%
3157
 
14.2%
482
 
7.4%
546
 
4.2%

JobSatisfaction
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
4
351 
3
335 
1
213 
2
203 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1102
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
4351
31.9%
3335
30.4%
1213
19.3%
2203
18.4%

Length

2022-10-02T13:18:36.880678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T13:18:36.952486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4351
31.9%
3335
30.4%
1213
19.3%
2203
18.4%

Most occurring characters

ValueCountFrequency (%)
4351
31.9%
3335
30.4%
1213
19.3%
2203
18.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1102
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4351
31.9%
3335
30.4%
1213
19.3%
2203
18.4%

Most occurring scripts

ValueCountFrequency (%)
Common1102
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4351
31.9%
3335
30.4%
1213
19.3%
2203
18.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4351
31.9%
3335
30.4%
1213
19.3%
2203
18.4%

MonthlyIncome
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1032
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6407.288566
Minimum1052
Maximum19926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2022-10-02T13:18:37.035264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1052
5-th percentile2090.3
Q12930
median4864
Q37938.25
95-th percentile17583.15
Maximum19926
Range18874
Interquartile range (IQR)5008.25

Descriptive statistics

Standard deviation4648.493197
Coefficient of variation (CV)0.7255008338
Kurtosis1.114586476
Mean6407.288566
Median Absolute Deviation (MAD)2098
Skewness1.406509959
Sum7060832
Variance21608489
MonotonicityNot monotonic
2022-10-02T13:18:37.129014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23424
 
0.4%
34523
 
0.3%
24043
 
0.3%
23803
 
0.3%
55623
 
0.3%
26103
 
0.3%
23402
 
0.2%
24062
 
0.2%
25872
 
0.2%
37602
 
0.2%
Other values (1022)1075
97.5%
ValueCountFrequency (%)
10521
0.1%
10811
0.1%
11021
0.1%
11291
0.1%
12001
0.1%
12231
0.1%
12611
0.1%
12741
0.1%
12811
0.1%
13591
0.1%
ValueCountFrequency (%)
199261
0.1%
198591
0.1%
198471
0.1%
198331
0.1%
197011
0.1%
196651
0.1%
196581
0.1%
196361
0.1%
196271
0.1%
196261
0.1%

MonthlyRate
Real number (ℝ≥0)

Distinct1078
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14488.54628
Minimum2094
Maximum26997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2022-10-02T13:18:37.226753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2094
5-th percentile3428.1
Q18468
median14388
Q320610.75
95-th percentile25547.9
Maximum26997
Range24903
Interquartile range (IQR)12142.75

Descriptive statistics

Standard deviation7044.849513
Coefficient of variation (CV)0.4862357739
Kurtosis-1.18123591
Mean14488.54628
Median Absolute Deviation (MAD)6071
Skewness-2.250635245 × 10-5
Sum15966378
Variance49629904.65
MonotonicityNot monotonic
2022-10-02T13:18:37.321499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21252
 
0.2%
42232
 
0.2%
115912
 
0.2%
161542
 
0.2%
219812
 
0.2%
193732
 
0.2%
41562
 
0.2%
117372
 
0.2%
91292
 
0.2%
104942
 
0.2%
Other values (1068)1082
98.2%
ValueCountFrequency (%)
20941
0.1%
20971
0.1%
21041
0.1%
21121
0.1%
21221
0.1%
21252
0.2%
22431
0.1%
22531
0.1%
23021
0.1%
23231
0.1%
ValueCountFrequency (%)
269971
0.1%
269681
0.1%
269591
0.1%
269561
0.1%
269141
0.1%
268941
0.1%
268621
0.1%
268491
0.1%
268411
0.1%
267671
0.1%

NumCompaniesWorked
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.653357532
Minimum0
Maximum9
Zeros144
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2022-10-02T13:18:37.399291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.472662995
Coefficient of variation (CV)0.931899665
Kurtosis0.1784642583
Mean2.653357532
Median Absolute Deviation (MAD)1
Skewness1.081826936
Sum2924
Variance6.114062286
MonotonicityNot monotonic
2022-10-02T13:18:37.461126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1400
36.3%
0144
 
13.1%
3123
 
11.2%
2110
 
10.0%
4105
 
9.5%
754
 
4.9%
547
 
4.3%
646
 
4.2%
942
 
3.8%
831
 
2.8%
ValueCountFrequency (%)
0144
 
13.1%
1400
36.3%
2110
 
10.0%
3123
 
11.2%
4105
 
9.5%
547
 
4.3%
646
 
4.2%
754
 
4.9%
831
 
2.8%
942
 
3.8%
ValueCountFrequency (%)
942
 
3.8%
831
 
2.8%
754
 
4.9%
646
 
4.2%
547
 
4.3%
4105
 
9.5%
3123
 
11.2%
2110
 
10.0%
1400
36.3%
0144
 
13.1%

PercentSalaryHike
Real number (ℝ≥0)

HIGH CORRELATION

Distinct15
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.25045372
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2022-10-02T13:18:37.826150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.699531695
Coefficient of variation (CV)0.2425850249
Kurtosis-0.4085876347
Mean15.25045372
Median Absolute Deviation (MAD)2
Skewness0.7871193708
Sum16806
Variance13.68653476
MonotonicityNot monotonic
2022-10-02T13:18:37.891974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11158
14.3%
13156
14.2%
12152
13.8%
14145
13.2%
1571
6.4%
1868
6.2%
1658
 
5.3%
1758
 
5.3%
1956
 
5.1%
2047
 
4.3%
Other values (5)133
12.1%
ValueCountFrequency (%)
11158
14.3%
12152
13.8%
13156
14.2%
14145
13.2%
1571
6.4%
1658
 
5.3%
1758
 
5.3%
1868
6.2%
1956
 
5.1%
2047
 
4.3%
ValueCountFrequency (%)
2513
 
1.2%
2416
 
1.5%
2321
 
1.9%
2244
4.0%
2139
3.5%
2047
4.3%
1956
5.1%
1868
6.2%
1758
5.3%
1658
5.3%

PerformanceRating
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
3
922 
4
180 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1102
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3922
83.7%
4180
 
16.3%

Length

2022-10-02T13:18:37.965777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T13:18:38.036588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3922
83.7%
4180
 
16.3%

Most occurring characters

ValueCountFrequency (%)
3922
83.7%
4180
 
16.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1102
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3922
83.7%
4180
 
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common1102
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3922
83.7%
4180
 
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3922
83.7%
4180
 
16.3%
Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
3
341 
4
321 
2
230 
1
210 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1102
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row3
4th row1
5th row4

Common Values

ValueCountFrequency (%)
3341
30.9%
4321
29.1%
2230
20.9%
1210
19.1%

Length

2022-10-02T13:18:38.096428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T13:18:38.168236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3341
30.9%
4321
29.1%
2230
20.9%
1210
19.1%

Most occurring characters

ValueCountFrequency (%)
3341
30.9%
4321
29.1%
2230
20.9%
1210
19.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1102
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3341
30.9%
4321
29.1%
2230
20.9%
1210
19.1%

Most occurring scripts

ValueCountFrequency (%)
Common1102
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3341
30.9%
4321
29.1%
2230
20.9%
1210
19.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3341
30.9%
4321
29.1%
2230
20.9%
1210
19.1%

StockOptionLevel
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
0
468 
1
447 
2
119 
3
68 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1102
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row0
4th row2
5th row2

Common Values

ValueCountFrequency (%)
0468
42.5%
1447
40.6%
2119
 
10.8%
368
 
6.2%

Length

2022-10-02T13:18:38.237052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T13:18:38.308860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0468
42.5%
1447
40.6%
2119
 
10.8%
368
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0468
42.5%
1447
40.6%
2119
 
10.8%
368
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1102
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0468
42.5%
1447
40.6%
2119
 
10.8%
368
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common1102
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0468
42.5%
1447
40.6%
2119
 
10.8%
368
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0468
42.5%
1447
40.6%
2119
 
10.8%
368
 
6.2%

TotalWorkingYears
Real number (ℝ≥0)

HIGH CORRELATION

Distinct40
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.16878403
Minimum0
Maximum40
Zeros6
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2022-10-02T13:18:38.385654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile27.95
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.682541289
Coefficient of variation (CV)0.6878583442
Kurtosis0.9763286901
Mean11.16878403
Median Absolute Deviation (MAD)4
Skewness1.115910411
Sum12308
Variance59.02144066
MonotonicityNot monotonic
2022-10-02T13:18:38.474417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10144
 
13.1%
687
 
7.9%
978
 
7.1%
877
 
7.0%
567
 
6.1%
162
 
5.6%
760
 
5.4%
450
 
4.5%
334
 
3.1%
1234
 
3.1%
Other values (30)409
37.1%
ValueCountFrequency (%)
06
 
0.5%
162
5.6%
227
 
2.5%
334
 
3.1%
450
4.5%
567
6.1%
687
7.9%
760
5.4%
877
7.0%
978
7.1%
ValueCountFrequency (%)
402
 
0.2%
381
 
0.1%
371
 
0.1%
365
0.5%
351
 
0.1%
344
0.4%
335
0.5%
327
0.6%
317
0.6%
304
0.4%

TrainingTimesLastYear
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.827586207
Minimum0
Maximum6
Zeros35
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2022-10-02T13:18:38.554204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.286851476
Coefficient of variation (CV)0.4551060097
Kurtosis0.4910451633
Mean2.827586207
Median Absolute Deviation (MAD)1
Skewness0.5955227246
Sum3116
Variance1.655986721
MonotonicityNot monotonic
2022-10-02T13:18:38.615041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2403
36.6%
3380
34.5%
592
 
8.3%
486
 
7.8%
154
 
4.9%
652
 
4.7%
035
 
3.2%
ValueCountFrequency (%)
035
 
3.2%
154
 
4.9%
2403
36.6%
3380
34.5%
486
 
7.8%
592
 
8.3%
652
 
4.7%
ValueCountFrequency (%)
652
 
4.7%
592
 
8.3%
486
 
7.8%
3380
34.5%
2403
36.6%
154
 
4.9%
035
 
3.2%

WorkLifeBalance
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
3
665 
2
257 
4
114 
1
 
66

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1102
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3665
60.3%
2257
 
23.3%
4114
 
10.3%
166
 
6.0%

Length

2022-10-02T13:18:38.685852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T13:18:38.756663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3665
60.3%
2257
 
23.3%
4114
 
10.3%
166
 
6.0%

Most occurring characters

ValueCountFrequency (%)
3665
60.3%
2257
 
23.3%
4114
 
10.3%
166
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1102
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3665
60.3%
2257
 
23.3%
4114
 
10.3%
166
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Common1102
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3665
60.3%
2257
 
23.3%
4114
 
10.3%
166
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3665
60.3%
2257
 
23.3%
4114
 
10.3%
166
 
6.0%

YearsAtCompany
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct37
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.08076225
Minimum0
Maximum40
Zeros28
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2022-10-02T13:18:38.832460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.176176149
Coefficient of variation (CV)0.8722473557
Kurtosis3.913260014
Mean7.08076225
Median Absolute Deviation (MAD)3
Skewness1.764805727
Sum7803
Variance38.14515183
MonotonicityNot monotonic
2022-10-02T13:18:38.922220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5147
13.3%
1133
12.1%
394
8.5%
293
8.4%
1087
7.9%
477
 
7.0%
771
 
6.4%
862
 
5.6%
961
 
5.5%
661
 
5.5%
Other values (27)216
19.6%
ValueCountFrequency (%)
028
 
2.5%
1133
12.1%
293
8.4%
394
8.5%
477
7.0%
5147
13.3%
661
5.5%
771
6.4%
862
5.6%
961
5.5%
ValueCountFrequency (%)
401
 
0.1%
371
 
0.1%
361
 
0.1%
341
 
0.1%
334
0.4%
321
 
0.1%
313
0.3%
301
 
0.1%
292
0.2%
272
0.2%

YearsInCurrentRole
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct19
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.261343013
Minimum0
Maximum18
Zeros179
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2022-10-02T13:18:39.007991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.61281567
Coefficient of variation (CV)0.8478115138
Kurtosis0.3688905098
Mean4.261343013
Median Absolute Deviation (MAD)3
Skewness0.8860130501
Sum4696
Variance13.05243707
MonotonicityNot monotonic
2022-10-02T13:18:39.077804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2278
25.2%
0179
16.2%
7168
15.2%
399
 
9.0%
481
 
7.4%
863
 
5.7%
954
 
4.9%
142
 
3.8%
529
 
2.6%
628
 
2.5%
Other values (9)81
 
7.4%
ValueCountFrequency (%)
0179
16.2%
142
 
3.8%
2278
25.2%
399
 
9.0%
481
 
7.4%
529
 
2.6%
628
 
2.5%
7168
15.2%
863
 
5.7%
954
 
4.9%
ValueCountFrequency (%)
181
 
0.1%
173
 
0.3%
164
 
0.4%
156
 
0.5%
1410
 
0.9%
1311
 
1.0%
127
 
0.6%
1118
 
1.6%
1021
 
1.9%
954
4.9%

YearsSinceLastPromotion
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct16
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.192377495
Minimum0
Maximum15
Zeros444
Zeros (%)40.3%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2022-10-02T13:18:39.151607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile10
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.274209901
Coefficient of variation (CV)1.493451701
Kurtosis3.673835339
Mean2.192377495
Median Absolute Deviation (MAD)1
Skewness2.009021088
Sum2416
Variance10.72045047
MonotonicityNot monotonic
2022-10-02T13:18:39.217431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0444
40.3%
1261
23.7%
2118
 
10.7%
760
 
5.4%
445
 
4.1%
341
 
3.7%
532
 
2.9%
622
 
2.0%
1120
 
1.8%
1511
 
1.0%
Other values (6)48
 
4.4%
ValueCountFrequency (%)
0444
40.3%
1261
23.7%
2118
 
10.7%
341
 
3.7%
445
 
4.1%
532
 
2.9%
622
 
2.0%
760
 
5.4%
810
 
0.9%
911
 
1.0%
ValueCountFrequency (%)
1511
 
1.0%
147
 
0.6%
138
 
0.7%
129
 
0.8%
1120
 
1.8%
103
 
0.3%
911
 
1.0%
810
 
0.9%
760
5.4%
622
 
2.0%

YearsWithCurrManager
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct18
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.176043557
Minimum0
Maximum17
Zeros192
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2022-10-02T13:18:39.288242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.590259245
Coefficient of variation (CV)0.8597274419
Kurtosis0.3702273593
Mean4.176043557
Median Absolute Deviation (MAD)3
Skewness0.8733560905
Sum4602
Variance12.88996144
MonotonicityNot monotonic
2022-10-02T13:18:39.356061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2258
23.4%
0192
17.4%
7166
15.1%
3107
9.7%
880
 
7.3%
476
 
6.9%
152
 
4.7%
946
 
4.2%
529
 
2.6%
621
 
1.9%
Other values (8)75
 
6.8%
ValueCountFrequency (%)
0192
17.4%
152
 
4.7%
2258
23.4%
3107
9.7%
476
 
6.9%
529
 
2.6%
621
 
1.9%
7166
15.1%
880
 
7.3%
946
 
4.2%
ValueCountFrequency (%)
177
 
0.6%
162
 
0.2%
155
 
0.5%
143
 
0.3%
139
 
0.8%
1215
 
1.4%
1117
 
1.5%
1017
 
1.5%
946
4.2%
880
7.3%

Attrition
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
False
939 
True
163 
ValueCountFrequency (%)
False939
85.2%
True163
 
14.8%
2022-10-02T13:18:39.432855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

BusinessTravel
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Travel_Rarely
784 
Travel_Frequently
205 
Non-Travel
113 

Length

Max length17
Median length13
Mean length13.43647913
Min length10

Characters and Unicode

Total characters14807
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Rarely
2nd rowTravel_Rarely
3rd rowTravel_Rarely
4th rowTravel_Rarely
5th rowTravel_Rarely

Common Values

ValueCountFrequency (%)
Travel_Rarely784
71.1%
Travel_Frequently205
 
18.6%
Non-Travel113
 
10.3%

Length

2022-10-02T13:18:39.498679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T13:18:39.577469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
travel_rarely784
71.1%
travel_frequently205
 
18.6%
non-travel113
 
10.3%

Most occurring characters

ValueCountFrequency (%)
e2296
15.5%
r2091
14.1%
l2091
14.1%
a1886
12.7%
T1102
7.4%
v1102
7.4%
y989
6.7%
_989
6.7%
R784
 
5.3%
n318
 
2.1%
Other values (7)1159
7.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter11501
77.7%
Uppercase Letter2204
 
14.9%
Connector Punctuation989
 
6.7%
Dash Punctuation113
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2296
20.0%
r2091
18.2%
l2091
18.2%
a1886
16.4%
v1102
9.6%
y989
8.6%
n318
 
2.8%
q205
 
1.8%
u205
 
1.8%
t205
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
T1102
50.0%
R784
35.6%
F205
 
9.3%
N113
 
5.1%
Connector Punctuation
ValueCountFrequency (%)
_989
100.0%
Dash Punctuation
ValueCountFrequency (%)
-113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13705
92.6%
Common1102
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2296
16.8%
r2091
15.3%
l2091
15.3%
a1886
13.8%
T1102
8.0%
v1102
8.0%
y989
7.2%
R784
 
5.7%
n318
 
2.3%
F205
 
1.5%
Other values (5)841
 
6.1%
Common
ValueCountFrequency (%)
_989
89.7%
-113
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII14807
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2296
15.5%
r2091
14.1%
l2091
14.1%
a1886
12.7%
T1102
7.4%
v1102
7.4%
y989
6.7%
_989
6.7%
R784
 
5.3%
n318
 
2.1%
Other values (7)1159
7.8%

Department
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Research & Development
727 
Sales
327 
Human Resources
 
48

Length

Max length22
Median length22
Mean length16.65063521
Min length5

Characters and Unicode

Total characters18349
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResearch & Development
2nd rowResearch & Development
3rd rowSales
4th rowResearch & Development
5th rowResearch & Development

Common Values

ValueCountFrequency (%)
Research & Development727
66.0%
Sales327
29.7%
Human Resources48
 
4.4%

Length

2022-10-02T13:18:39.650274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T13:18:39.728066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
research727
27.9%
727
27.9%
development727
27.9%
sales327
12.6%
human48
 
1.8%
resources48
 
1.8%

Most occurring characters

ValueCountFrequency (%)
e4058
22.1%
1502
 
8.2%
s1150
 
6.3%
a1102
 
6.0%
l1054
 
5.7%
R775
 
4.2%
r775
 
4.2%
c775
 
4.2%
n775
 
4.2%
m775
 
4.2%
Other values (10)5608
30.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14243
77.6%
Uppercase Letter1877
 
10.2%
Space Separator1502
 
8.2%
Other Punctuation727
 
4.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4058
28.5%
s1150
 
8.1%
a1102
 
7.7%
l1054
 
7.4%
r775
 
5.4%
c775
 
5.4%
n775
 
5.4%
m775
 
5.4%
o775
 
5.4%
p727
 
5.1%
Other values (4)2277
16.0%
Uppercase Letter
ValueCountFrequency (%)
R775
41.3%
D727
38.7%
S327
17.4%
H48
 
2.6%
Space Separator
ValueCountFrequency (%)
1502
100.0%
Other Punctuation
ValueCountFrequency (%)
&727
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16120
87.9%
Common2229
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4058
25.2%
s1150
 
7.1%
a1102
 
6.8%
l1054
 
6.5%
R775
 
4.8%
r775
 
4.8%
c775
 
4.8%
n775
 
4.8%
m775
 
4.8%
o775
 
4.8%
Other values (8)4106
25.5%
Common
ValueCountFrequency (%)
1502
67.4%
&727
32.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII18349
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4058
22.1%
1502
 
8.2%
s1150
 
6.3%
a1102
 
6.0%
l1054
 
5.7%
R775
 
4.2%
r775
 
4.2%
c775
 
4.2%
n775
 
4.2%
m775
 
4.2%
Other values (10)5608
30.6%

EducationField
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Life Sciences
450 
Medical
355 
Marketing
114 
Technical Degree
105 
Other
58 

Length

Max length16
Median length15
Mean length10.55444646
Min length5

Characters and Unicode

Total characters11631
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLife Sciences
2nd rowMedical
3rd rowMarketing
4th rowLife Sciences
5th rowLife Sciences

Common Values

ValueCountFrequency (%)
Life Sciences450
40.8%
Medical355
32.2%
Marketing114
 
10.3%
Technical Degree105
 
9.5%
Other58
 
5.3%
Human Resources20
 
1.8%

Length

2022-10-02T13:18:39.795885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T13:18:39.876695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
life450
26.8%
sciences450
26.8%
medical355
21.2%
marketing114
 
6.8%
technical105
 
6.3%
degree105
 
6.3%
other58
 
3.5%
human20
 
1.2%
resources20
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e2337
20.1%
c1485
12.8%
i1474
12.7%
n689
 
5.9%
a594
 
5.1%
575
 
4.9%
s490
 
4.2%
M469
 
4.0%
l460
 
4.0%
L450
 
3.9%
Other values (16)2608
22.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9379
80.6%
Uppercase Letter1677
 
14.4%
Space Separator575
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2337
24.9%
c1485
15.8%
i1474
15.7%
n689
 
7.3%
a594
 
6.3%
s490
 
5.2%
l460
 
4.9%
f450
 
4.8%
d355
 
3.8%
r297
 
3.2%
Other values (7)748
 
8.0%
Uppercase Letter
ValueCountFrequency (%)
M469
28.0%
L450
26.8%
S450
26.8%
T105
 
6.3%
D105
 
6.3%
O58
 
3.5%
H20
 
1.2%
R20
 
1.2%
Space Separator
ValueCountFrequency (%)
575
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11056
95.1%
Common575
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2337
21.1%
c1485
13.4%
i1474
13.3%
n689
 
6.2%
a594
 
5.4%
s490
 
4.4%
M469
 
4.2%
l460
 
4.2%
L450
 
4.1%
f450
 
4.1%
Other values (15)2158
19.5%
Common
ValueCountFrequency (%)
575
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11631
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2337
20.1%
c1485
12.8%
i1474
12.7%
n689
 
5.9%
a594
 
5.1%
575
 
4.9%
s490
 
4.2%
M469
 
4.0%
l460
 
4.0%
L450
 
3.9%
Other values (16)2608
22.4%

Gender
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Male
667 
Female
435 

Length

Max length6
Median length4
Mean length4.789473684
Min length4

Characters and Unicode

Total characters5278
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male667
60.5%
Female435
39.5%

Length

2022-10-02T13:18:39.963470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T13:18:40.042257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
male667
60.5%
female435
39.5%

Most occurring characters

ValueCountFrequency (%)
e1537
29.1%
a1102
20.9%
l1102
20.9%
M667
12.6%
F435
 
8.2%
m435
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4176
79.1%
Uppercase Letter1102
 
20.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1537
36.8%
a1102
26.4%
l1102
26.4%
m435
 
10.4%
Uppercase Letter
ValueCountFrequency (%)
M667
60.5%
F435
39.5%

Most occurring scripts

ValueCountFrequency (%)
Latin5278
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1537
29.1%
a1102
20.9%
l1102
20.9%
M667
12.6%
F435
 
8.2%
m435
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII5278
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1537
29.1%
a1102
20.9%
l1102
20.9%
M667
12.6%
F435
 
8.2%
m435
 
8.2%

JobRole
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Sales Executive
240 
Research Scientist
213 
Laboratory Technician
204 
Manufacturing Director
111 
Healthcare Representative
99 
Other values (4)
235 

Length

Max length25
Median length21
Mean length18.13339383
Min length7

Characters and Unicode

Total characters19983
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLaboratory Technician
2nd rowResearch Director
3rd rowSales Executive
4th rowResearch Scientist
5th rowResearch Scientist

Common Values

ValueCountFrequency (%)
Sales Executive240
21.8%
Research Scientist213
19.3%
Laboratory Technician204
18.5%
Manufacturing Director111
10.1%
Healthcare Representative99
9.0%
Manager74
 
6.7%
Research Director60
 
5.4%
Sales Representative59
 
5.4%
Human Resources42
 
3.8%

Length

2022-10-02T13:18:40.111073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T13:18:40.205822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
sales299
14.0%
research273
12.8%
executive240
11.3%
scientist213
10.0%
laboratory204
9.6%
technician204
9.6%
director171
8.0%
representative158
7.4%
manufacturing111
 
5.2%
healthcare99
 
4.6%
Other values (3)158
7.4%

Most occurring characters

ValueCountFrequency (%)
e2901
14.5%
a1952
 
9.8%
t1567
 
7.8%
c1557
 
7.8%
i1514
 
7.6%
r1507
 
7.5%
n1117
 
5.6%
1028
 
5.1%
s1027
 
5.1%
o621
 
3.1%
Other values (19)5192
26.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter16825
84.2%
Uppercase Letter2130
 
10.7%
Space Separator1028
 
5.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2901
17.2%
a1952
11.6%
t1567
9.3%
c1557
9.3%
i1514
9.0%
r1507
9.0%
n1117
 
6.6%
s1027
 
6.1%
o621
 
3.7%
h576
 
3.4%
Other values (10)2486
14.8%
Uppercase Letter
ValueCountFrequency (%)
S512
24.0%
R473
22.2%
E240
11.3%
L204
 
9.6%
T204
 
9.6%
M185
 
8.7%
D171
 
8.0%
H141
 
6.6%
Space Separator
ValueCountFrequency (%)
1028
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin18955
94.9%
Common1028
 
5.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2901
15.3%
a1952
10.3%
t1567
 
8.3%
c1557
 
8.2%
i1514
 
8.0%
r1507
 
8.0%
n1117
 
5.9%
s1027
 
5.4%
o621
 
3.3%
h576
 
3.0%
Other values (18)4616
24.4%
Common
ValueCountFrequency (%)
1028
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII19983
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2901
14.5%
a1952
 
9.8%
t1567
 
7.8%
c1557
 
7.8%
i1514
 
7.6%
r1507
 
7.5%
n1117
 
5.6%
1028
 
5.1%
s1027
 
5.1%
o621
 
3.1%
Other values (19)5192
26.0%

MaritalStatus
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Married
494 
Single
353 
Divorced
255 

Length

Max length8
Median length7
Mean length6.91107078
Min length6

Characters and Unicode

Total characters7616
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDivorced
2nd rowMarried
3rd rowMarried
4th rowDivorced
5th rowDivorced

Common Values

ValueCountFrequency (%)
Married494
44.8%
Single353
32.0%
Divorced255
23.1%

Length

2022-10-02T13:18:40.307549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T13:18:40.387335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
married494
44.8%
single353
32.0%
divorced255
23.1%

Most occurring characters

ValueCountFrequency (%)
r1243
16.3%
i1102
14.5%
e1102
14.5%
d749
9.8%
M494
 
6.5%
a494
 
6.5%
S353
 
4.6%
n353
 
4.6%
g353
 
4.6%
l353
 
4.6%
Other values (4)1020
13.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6514
85.5%
Uppercase Letter1102
 
14.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r1243
19.1%
i1102
16.9%
e1102
16.9%
d749
11.5%
a494
 
7.6%
n353
 
5.4%
g353
 
5.4%
l353
 
5.4%
v255
 
3.9%
o255
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
M494
44.8%
S353
32.0%
D255
23.1%

Most occurring scripts

ValueCountFrequency (%)
Latin7616
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r1243
16.3%
i1102
14.5%
e1102
14.5%
d749
9.8%
M494
 
6.5%
a494
 
6.5%
S353
 
4.6%
n353
 
4.6%
g353
 
4.6%
l353
 
4.6%
Other values (4)1020
13.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII7616
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r1243
16.3%
i1102
14.5%
e1102
14.5%
d749
9.8%
M494
 
6.5%
a494
 
6.5%
S353
 
4.6%
n353
 
4.6%
g353
 
4.6%
l353
 
4.6%
Other values (4)1020
13.4%

OverTime
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
No
806 
Yes
296 

Length

Max length3
Median length2
Mean length2.268602541
Min length2

Characters and Unicode

Total characters2500
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No806
73.1%
Yes296
 
26.9%

Length

2022-10-02T13:18:40.458150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T13:18:40.530952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no806
73.1%
yes296
 
26.9%

Most occurring characters

ValueCountFrequency (%)
N806
32.2%
o806
32.2%
Y296
 
11.8%
e296
 
11.8%
s296
 
11.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1398
55.9%
Uppercase Letter1102
44.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o806
57.7%
e296
 
21.2%
s296
 
21.2%
Uppercase Letter
ValueCountFrequency (%)
N806
73.1%
Y296
 
26.9%

Most occurring scripts

ValueCountFrequency (%)
Latin2500
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N806
32.2%
o806
32.2%
Y296
 
11.8%
e296
 
11.8%
s296
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N806
32.2%
o806
32.2%
Y296
 
11.8%
e296
 
11.8%
s296
 
11.8%

Interactions

2022-10-02T13:18:33.092694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:14.145573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:15.505485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:16.756170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:18.219260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:19.496845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:20.803385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:22.133029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:23.581224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:24.823935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:26.131440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:27.469870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:28.766403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:30.367279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:31.745250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:33.186442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:14.247317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:15.589228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:17.020465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:18.304034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:19.584610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:20.893145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:22.215818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:23.669987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:24.911728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:26.222196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:27.558634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:28.856190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:30.459035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:31.835042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:33.268224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:14.331078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:15.665025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:17.101248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:18.383820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:19.663400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:20.975924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:22.292603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:23.744788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:24.990517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:26.304976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:27.637422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:28.938969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:30.543807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:31.918796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:33.358981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:14.420838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:15.747804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:17.185036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:18.468593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:19.749177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:21.064686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:22.375407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:23.825589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:25.075278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:26.394735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:27.723192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:29.025711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:30.649525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:32.009550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:33.447761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:14.517606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:15.828589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:17.269825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:18.552400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:19.834940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:21.152489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:22.653638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:23.909381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:25.160063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:26.483511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:27.807966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:29.114474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:30.739286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:32.097317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:33.537505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:14.606360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:15.914359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:17.357581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:18.640135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:19.923729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:21.242213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:22.736417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:23.994153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:25.247836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:26.575253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:27.894734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:29.204235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:30.835092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:32.189070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:33.627290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:14.697100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:16.000130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:17.446337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:18.727899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:20.013464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:21.332969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:22.822189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:24.079925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:25.336595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:26.667037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:27.982500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:29.294991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:30.930837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:32.286810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:33.711040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:14.783868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:16.077923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:17.526113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:18.807686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:20.094247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:21.415748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:22.899980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:24.157717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:25.417348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:26.751781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:28.064281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:29.617130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:31.020597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:32.368590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:33.793818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:14.870640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:16.156712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:17.604901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:18.887478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:20.174034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:21.497530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:22.976773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:24.233513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:25.497136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:26.834570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:28.144067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:29.697915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:31.106870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:32.454360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:33.881584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:14.962398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:16.245535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:17.689675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:18.971250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:20.260801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:21.585295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:23.059552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:24.313300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:25.584901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:26.924328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:28.228842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:29.786705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:31.193641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:32.544146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:33.975393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:15.060135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:16.340308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:17.779436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:19.062007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:20.350562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:21.678074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:23.146320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:24.399071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:25.682639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:27.016083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:28.318628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:29.879524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:31.287449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:32.636933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:34.064155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:15.146413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:16.421066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:17.864228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:19.146779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:20.446305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:21.766012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:23.229099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:24.480878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:25.769408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:27.103850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:28.401380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:29.967290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:31.375213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:32.724672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:34.156907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:15.237170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:16.505839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:17.953969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:19.235573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:20.537063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:21.857765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:23.315867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:24.565630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:25.860191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:27.196600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:28.503108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:30.061069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:31.467988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:32.815454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:34.246667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:15.325933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:16.589615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:18.042757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:19.323319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:20.625859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:21.950516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:23.400640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:24.651397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:25.951954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:27.286361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:28.590874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:30.169806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:31.559721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:32.905188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:34.338421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:15.414704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:16.672394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:18.129503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:19.409078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:20.714648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:22.041274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:23.490427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:24.737169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:26.041680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:27.377144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:28.677651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:30.263557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:31.651476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T13:18:32.992985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-10-02T13:18:40.596843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-02T13:18:40.750827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-02T13:18:40.901424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-02T13:18:41.056010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-02T13:18:41.225557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-02T13:18:34.519968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-02T13:18:35.186185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexAgeDailyRateDistanceFromHomeEducationEnvironmentSatisfactionHourlyRateJobInvolvementJobLevelJobSatisfactionMonthlyIncomeMonthlyRateNumCompaniesWorkedPercentSalaryHikePerformanceRatingRelationshipSatisfactionStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerAttritionBusinessTravelDepartmentEducationFieldGenderJobRoleMaritalStatusOverTime
073013582414673132693133351224211231000NoTravel_RarelyResearch & DevelopmentLife SciencesMaleLaboratory TechnicianDivorcedNo
112645547823360251190381980581232334231000NoTravel_RarelyResearch & DevelopmentMedicalMaleResearch DirectorMarriedNo
24473561913285323471718659911330152311969NoTravel_RarelySalesMarketingMaleSales ExecutiveMarriedNo
324241141119233632130721987721631217221000NoTravel_RarelyResearch & DevelopmentLife SciencesMaleResearch ScientistDivorcedNo
4938588482341883132372260761123422332222NoTravel_RarelyResearch & DevelopmentLife SciencesMaleResearch ScientistDivorcedNo
55444721733449343137701022591234228222221113NoTravel_FrequentlySalesMedicalFemaleSales ExecutiveDivorcedYes
626428529241793133485149352113305510000YesTravel_RarelyResearch & DevelopmentLife SciencesMaleLaboratory TechnicianSingleNo
79164616842433252187899946214331262311408NoTravel_RarelySalesMarketingFemaleManagerMarriedNo
81484193394394311223869612214417235014NoTravel_RarelyResearch & DevelopmentLife SciencesMaleLaboratory TechnicianMarriedNo
98653013292943613214115131928193338334303NoTravel_RarelySalesLife SciencesMaleSales ExecutiveDivorcedNo

Last rows

df_indexAgeDailyRateDistanceFromHomeEducationEnvironmentSatisfactionHourlyRateJobInvolvementJobLevelJobSatisfactionMonthlyIncomeMonthlyRateNumCompaniesWorkedPercentSalaryHikePerformanceRatingRelationshipSatisfactionStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerAttritionBusinessTravelDepartmentEducationFieldGenderJobRoleMaritalStatusOverTime
1092143823638934333111790269561193111321010YesTravel_FrequentlySalesMarketingMaleSales RepresentativeMarriedNo
1093878421792547942162721285871631110344303NoNon-TravelHuman ResourcesMedicalMaleHuman ResourcesMarriedNo
1094121137127814331124952576771143326226313NoTravel_FrequentlySalesMedicalMaleSales ExecutiveDivorcedNo
109565729108671162214253260546143338534303NoTravel_RarelyResearch & DevelopmentMedicalFemaleLaboratory TechnicianDivorcedNo
1096307388492521812321206126707317331192310801NoTravel_RarelyResearch & DevelopmentLife SciencesFemaleResearch DirectorMarriedNo
10971632334521804123298150530123427526205NoTravel_RarelyResearch & DevelopmentLife SciencesMaleResearch ScientistDivorcedYes
10985831655744483245915952832244110327717NoTravel_RarelyResearch & DevelopmentLife SciencesMaleLaboratory TechnicianDivorcedNo
109927738322721444215605191911244318338077NoTravel_RarelySalesMedicalFemaleSales ExecutiveDivorcedYes
11002552568513162323489875050123425334212NoTravel_RarelyResearch & DevelopmentLife SciencesFemaleManufacturing DirectorMarriedNo
11011344377837447832142841358852243116235304NoTravel_RarelyResearch & DevelopmentMedicalMaleResearch ScientistMarriedYes